Multi-energy collaborative scheduling optimization emission reduction system for power plant

Abstract

The invention relates to the technical field of energy scheduling optimization, in particular to a power plant multi-energy collaborative scheduling optimization emission reduction system, which comprises a constraint construction unit, a first target construction unit, a second target construction unit, a third target construction unit, a fourth target construction unit and a target optimization unit, and further comprises a supply modeling unit and a power plant multi-energy collaborative scheduling optimization emission reduction unit, the supply modeling unit is used for modeling energy supply of a target power plant to obtain an energy supply model of the target power plant, acquiring the energy supply quantity of the target power plant based on the energy supply model of the target power plant, and transmitting the energy supply quantity of the target power plant to the constraint construction unit. According to the invention, the scheduling scheme of the power plant can be comprehensively optimized by integrating multiple target construction units including the operation cost, the carbon transaction cost, the pollutant discharge and the day-ahead cost, and the multi-target optimization can better balance the economic benefit and the environmental influence, so that the overall benefit of the operation of the power plant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling optimization, and in particular to a multi-energy coordinated scheduling optimization and emission reduction system for a power plant. Background Art

[0002] Multi-energy power plant refers to the mode of using multiple energy types to generate and supply electricity during power plant operation. Unlike single-energy power plants, multi-energy power plants can integrate and coordinate different types of energy resources to achieve more efficient, economical and environmentally friendly operation.

[0003] Traditional systems often only focus on cost or efficiency and lack the ability to comprehensively consider environmental impacts. The accuracy of energy supply and demand forecasting and modeling is limited, which can easily lead to energy waste or insufficient supply. The control of pollutant emissions is not strict enough to meet increasingly stringent environmental protection standards. Carbon trading costs and other indirect costs are not fully considered, resulting in poor economic benefits. Traditional systems have poor adaptability to the actual operating environment and limited ability to handle complex constraints. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose a multi-energy coordinated scheduling and optimized emission reduction system for power plants.

[0005] The technical solution of the present invention is a multi-energy coordinated dispatch optimization and emission reduction system for a power plant, comprising a constraint construction unit, a first target construction unit, a second target construction unit, a third target construction unit, a fourth target construction unit and a target optimization unit, and further comprising:

[0006] A supply modeling unit, the supply modeling unit is used to model the energy supply of the target power plant to obtain an energy supply model of the target power plant, obtain the energy supply amount of the target power plant based on the energy supply model of the target power plant, and transmit the energy supply amount of the target power plant to the constraint construction unit;

[0007] A load forecasting unit, the load forecasting unit is used to perform load forecasting on the load end of the target power plant to obtain a load forecast amount of the load end of the target power plant, and transmit the load forecast amount of the load end of the target power plant to a demand modeling unit;

[0008] A demand modeling unit, wherein the demand modeling unit receives the load forecast amount of the load end of the target power plant transmitted by the load forecasting unit, and models the energy demand of the load end of the target power plant based on the load forecast amount of the load end of the target power plant to obtain an energy demand model of the load end of the target power plant, obtains the energy demand of the load end of the target power plant based on the energy demand model of the load end of the target power plant, and transmits the energy demand of the load end of the target power plant to the constraint construction unit.

[0009] Preferably, the first target construction unit is used to model the operating cost of the target power plant to obtain an operating cost target model of the target power plant, and transmit the operating cost target model of the target power plant to the target optimization unit;

[0010] The second target construction unit is used to model the carbon trading cost of the target power plant to obtain the carbon trading cost target model of the target power plant, and transmit the carbon trading cost target model of the target power plant to the target optimization unit.

[0011] Preferably, the third target construction unit is used to model the pollutant emissions of the target power plant to obtain a pollutant emission target model of the target power plant, and transmit the pollutant emission target model of the target power plant to the target optimization unit;

[0012] The fourth target construction unit is used to model the day-ahead cost of the target power plant to obtain the day-ahead cost target model of the target power plant, and transmit the day-ahead cost target model of the target power plant to the target optimization unit.

[0013] Preferably, the constraint construction unit receives the energy supply of the target power plant transmitted by the supply modeling unit and the energy demand of the load end of the target power plant transmitted by the demand modeling unit, and constructs the constraint conditions of the target power plant based on the energy supply of the target power plant and the energy demand of the load end of the target power plant, and transmits the constraint conditions of the target power plant to the target optimization unit;

[0014] The target optimization unit receives the operating cost target model of the target power plant transmitted by the first target construction unit, the carbon trading cost target model of the target power plant transmitted by the second target construction unit, the pollutant emission target model of the target power plant transmitted by the third target construction unit, the day-ahead cost target model of the target power plant transmitted by the fourth target construction unit, and the constraint conditions of the target power plant transmitted by the constraint construction unit, and constructs a target optimization model based on the operating cost target model, carbon trading cost target model, pollutant emission target model, day-ahead cost target model and constraint conditions of the target power plant, and solves the target optimization model based on the optimization algorithm to obtain a scheduling plan for the target power plant.

[0015] Preferably, the energy supply model of the target power plant is as follows:

[0016]

[0017] Among them, Q s,t represents the energy supply of the target power plant in period t, I g ,Ir and I h Respectively represent the total number of thermal power generating units, gas generating units and hydropower generating units in the target power plant, and They represent the upper and lower limits of the output of thermal power generating units in the target power plant, and They represent the upper and lower limits of the output of the gas-fired generator sets in the target power plant, and They represent the upper and lower limits of the output of the hydroelectric generating units in the target power plant during period t, M g 、M r and M h They respectively represent the climbing capabilities of the thermal power generating units, gas power generating units and hydropower generating units in the target power plant.

[0018] Preferably, the energy demand model of the load end of the target power plant is as follows:

[0019]

[0020] Among them, Q d,t represents the energy demand at the load end of the target power plant during period t, q g ,q r and q h They represent the adjustment coefficients caused by the load power prediction errors of the thermal power generating units, gas generating units and hydropower generating units in the target power plant, respectively. and They represent the load power forecast of thermal power generating units, gas generating units and hydropower generating units in the target power plant in the period t+1 respectively, e represents the adjustment coefficient caused by the load forecast error at the load end of the target power plant, and They represent the load forecast of the target power plant at the load end in the t+1 period and the t period respectively.

[0021] Preferably, the target operating cost model of the target power plant is as follows:

[0022]

[0023] Among them, min:C ope represents the target power plant operating cost target model, c g 、c r and c h They represent the unit energy purchase costs of thermal power generating units, gas generating units and hydropower generating units in the target power plant respectively, and T represents the preset total time period;

[0024] The carbon trading cost target model of the target power plant is as follows:

[0025]

[0026] Among them, min:C reg represents the carbon trading cost target model of the target power plant, c r,g 、c r,r and c r,g Respectively represent the carbon trading prices of thermal power generation units, gas power generation units and hydropower generation units in the target power plant, ξ g , r and h They respectively represent the carbon emission coefficient per unit of electricity of thermal power generating units, gas power generating units and hydropower generating units in the target power plant.

[0027] Preferably, the pollutant emission target model of the target power plant is as follows:

[0028]

[0029] Among them, min:F pol represents the pollutant emission target model of the target power plant, a1, a2, a3, a4 and a5 represent the pollutant emission coefficients of the thermal power generating units in the target power plant, and b1, b2, b3, b4 and b5 represent the pollutant emission coefficients of the gas generating units in the target power plant;

[0030] The day-ahead cost target model of the target power plant is as follows:

[0031]

[0032] Among them, min:F day represents the day-ahead cost target model of the target power plant, u g,t 、u r,t and u h,t are Boolean variables representing the operating status of the thermal power generation unit, gas power generation unit and hydropower generation unit in the target power plant during period t. The value is 1 if the operating status is on and 0 if the operating status is off. g,on , C r,on and C h,on They represent the single startup costs of thermal power generating units, gas generating units and hydropower generating units in the target power plant, respectively. g,off , C r,off and C h,off They represent the single shutdown costs of thermal power generating units, gas power generating units and hydropower generating units in the target power plant respectively.

[0033] Preferably, the constraints of the target power plant are as follows:

[0034]

[0035] Wherein, st: represents the constraint condition of the target power plant.

[0036] Preferably, solving the target optimization model based on an optimization algorithm to obtain a scheduling plan for the target power plant includes:

[0037] Set the initialization parameters of the optimization algorithm to initialize the population position. The population position initialization formula is as follows:

[0038]

[0039] Among them, x t and x t+1 represents the position of the individual at the t-th iteration and the t+1-th iteration, and p represents the preset parameter for controlling the nonlinear mapping;

[0040] Calculate the fitness value of the individual and calculate the elite individual position. The elite individual position calculation formula is as follows:

[0041]

[0042] in, represents the reverse solution of the j-th dimension individual at the t-th iteration, x i,j (t) represents the elite solution of the j-th dimension individual at the t-th iteration, k represents a random number in the interval [0,1], u j (t) represents the maximum position of the individual at the tth iteration, k j (t) represents the minimum position of the individual at the tth iteration;

[0043] Marking a preset number of the individuals with the best fitness values ​​as leader individuals, and updating the positions of the remaining individuals in the population;

[0044] Calculate and sort the updated individual fitness values, obtain elite individuals, and update the elite individual positions;

[0045] Repeat the above steps until the optimal solution is found or the maximum number of iterations of the optimization algorithm is reached, and the scheduling plan of the target power plant is obtained.

[0046] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0047] 1. The present invention can comprehensively optimize the dispatching plan of the power plant by integrating multiple target construction units such as operating cost, carbon trading cost, pollutant emission and day-ahead cost. This multi-target optimization can better balance economic benefits and environmental impact, thereby improving the overall efficiency of the power plant operation. Moreover, through the supply modeling unit and the demand modeling unit, the energy supply and demand of the power plant can be accurately modeled and predicted. This precise matching of demand and supply can avoid energy waste and insufficient supply, and improve the stability and reliability of the power plant operation. Moreover, through the third target construction unit, the modeling of pollutant emission is focused on, so that the emission of pollutants can be effectively controlled and reduced while optimizing the dispatching plan, which helps the power plant meet environmental protection requirements in operation and reduce the negative impact on the environment.

[0048] 2. The present invention models the operating cost and the carbon trading cost respectively through the first target construction unit and the second target construction unit, which can help the power plant control and reduce various costs and improve economic benefits during the scheduling process. The constraint construction unit can establish corresponding constraints according to the actual situation of energy supply and demand, so that the scheduling plan can better adapt to the specific operating environment and requirements of the power plant in actual operation. The target optimization unit comprehensively considers multiple goals and constraints and uses the optimization algorithm to solve the problem, so as to obtain the optimal scheduling plan. This optimization solution method ensures the optimality of the scheduling plan in terms of economy, environmental protection and operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the system architecture of an overall system in an embodiment of the present invention.

[0050] Figure numerals: 1. Supply modeling unit; 2. Constraint building unit; 3. Load forecasting unit; 4. Demand modeling unit; 5. First target building unit; 6. Second target building unit; 7. Third target building unit; 8. Fourth target building unit; 9. Target optimization unit. DETAILED DESCRIPTION

[0051] Embodiment 1, as Figure 1 As shown, a multi-energy coordinated dispatch optimization and emission reduction system for a power plant proposed by the present invention includes a constraint construction unit 2, a first target construction unit 5, a second target construction unit 6, a third target construction unit 7, a fourth target construction unit 8 and a target optimization unit 9, and also includes:

[0052] The supply modeling unit 1 is used to model the energy supply of the target power plant to obtain the energy supply model of the target power plant, obtain the energy supply amount of the target power plant based on the energy supply model of the target power plant, and transmit the energy supply amount of the target power plant to the constraint building unit 2;

[0053] The load prediction unit 3 is used to perform load prediction on the load end of the target power plant to obtain the load prediction amount of the load end of the target power plant, and transmit the load prediction amount of the load end of the target power plant to the demand modeling unit 4;

[0054] The demand modeling unit 4 receives the load forecast amount of the load end of the target power plant transmitted by the load forecasting unit 3, and models the energy demand of the load end of the target power plant based on the load forecast amount of the load end of the target power plant to obtain the energy demand model of the load end of the target power plant, obtains the energy demand of the load end of the target power plant based on the energy demand model of the load end of the target power plant, and transmits the energy demand of the load end of the target power plant to the constraint construction unit 2.

[0055] In the present invention, a long short-term memory network (LSTM) is used to predict the load at the load end of the target power plant. The long short-term memory network (LSTM) is a special recurrent neural network (RNN) that can process and predict time series data with long-term dependencies.

[0056] In an optional embodiment, the first target construction unit 5 is used to model the operating cost of the target power plant to obtain an operating cost target model of the target power plant, and transmit the operating cost target model of the target power plant to the target optimization unit 9;

[0057] The second target construction unit 6 is used to model the carbon trading cost of the target power plant to obtain the carbon trading cost target model of the target power plant, and transmit the carbon trading cost target model of the target power plant to the target optimization unit 9.

[0058] In an optional embodiment, the third target construction unit 7 is used to model the pollutant emissions of the target power plant to obtain a pollutant emission target model of the target power plant, and transmit the pollutant emission target model of the target power plant to the target optimization unit 9;

[0059] The fourth target construction unit 8 is used to model the day-ahead cost of the target power plant to obtain the day-ahead cost target model of the target power plant, and transmit the day-ahead cost target model of the target power plant to the target optimization unit 9 .

[0060] In an optional embodiment, the constraint construction unit 2 receives the energy supply of the target power plant transmitted by the supply modeling unit 1 and the energy demand of the load end of the target power plant transmitted by the demand modeling unit 4, and constructs the constraint conditions of the target power plant based on the energy supply of the target power plant and the energy demand of the load end of the target power plant, and transmits the constraint conditions of the target power plant to the target optimization unit 9;

[0061] The target optimization unit 9 receives the operating cost target model of the target power plant transmitted by the first target construction unit 5, the carbon trading cost target model of the target power plant transmitted by the second target construction unit 6, the pollutant emission target model of the target power plant transmitted by the third target construction unit 7, the day-ahead cost target model of the target power plant transmitted by the fourth target construction unit 8, and the constraint conditions of the target power plant transmitted by the constraint construction unit 2, and constructs a target optimization model based on the operating cost target model, carbon trading cost target model, pollutant emission target model, day-ahead cost target model and constraint conditions of the target power plant, and solves the target optimization model based on the optimization algorithm to obtain a scheduling plan for the target power plant.

[0062] Embodiment 2, a multi-energy coordinated dispatch optimization and emission reduction system for a power plant proposed by the present invention, compared with embodiment 1, this embodiment further includes: the energy supply model of the target power plant is as follows:

[0063]

[0064] Among them, Q s,t represents the energy supply of the target power plant in period t, I g ,I r and I h Respectively represent the total number of thermal power generating units, gas generating units and hydropower generating units in the target power plant, and They represent the upper and lower limits of the output of thermal power generating units in the target power plant, and They represent the upper and lower limits of the output of the gas-fired generator sets in the target power plant, and They represent the upper and lower limits of the output of the hydroelectric generating units in the target power plant during period t, M g 、M r and M h They respectively represent the climbing capabilities of the thermal power generating units, gas power generating units and hydropower generating units in the target power plant.

[0065] In an optional embodiment, the energy demand model of the load side of the target power plant is as follows:

[0066]

[0067] Among them, Q d,t represents the energy demand at the load end of the target power plant during period t, q g ,q r and q h They represent the adjustment coefficients caused by the load power prediction errors of the thermal power generating units, gas generating units and hydropower generating units in the target power plant, respectively. and They represent the load power forecast of thermal power generating units, gas generating units and hydropower generating units in the target power plant in the period t+1 respectively, e represents the adjustment coefficient caused by the load forecast error at the load end of the target power plant, and They represent the load forecast of the target power plant at the load end in the t+1 period and the t period respectively.

[0068] In an optional embodiment, the target operating cost model of the target power plant is as follows:

[0069]

[0070] Among them, min:C ope represents the target power plant operating cost target model, c g 、c r and c h They represent the unit energy purchase costs of thermal power generating units, gas generating units and hydropower generating units in the target power plant respectively, and T represents the preset total time period;

[0071] The carbon trading cost target model of the target power plant is as follows:

[0072]

[0073] Among them, min:C reg represents the carbon trading cost target model of the target power plant, c r,g 、c r,r and c r,g Respectively represent the carbon trading prices of thermal power generation units, gas power generation units and hydropower generation units in the target power plant, ξ g , r and h They respectively represent the carbon emission coefficient per unit of electricity of thermal power generating units, gas power generating units and hydropower generating units in the target power plant.

[0074] In an optional embodiment, the pollutant emission target model of the target power plant is as follows:

[0075]

[0076] Among them, min:F pol represents the pollutant emission target model of the target power plant, a1, a2, a3, a4 and a5 represent the pollutant emission coefficients of the thermal power generating units in the target power plant, and b1, b2, b3, b4 and b5 represent the pollutant emission coefficients of the gas generating units in the target power plant;

[0077] The day-ahead cost target model of the target power plant is as follows:

[0078]

[0079] Among them, min:F day represents the day-ahead cost target model of the target power plant, u g,t 、u r,t and u h,t are Boolean variables representing the operating status of the thermal power generation unit, gas power generation unit and hydropower generation unit in the target power plant during period t. The value is 1 if the operating status is on and 0 if the operating status is off. g,on , C r,on and C h,on They represent the single startup costs of thermal power generating units, gas generating units and hydropower generating units in the target power plant, respectively. g,off , C r,off and C h,off They represent the single shutdown costs of thermal power generating units, gas power generating units and hydropower generating units in the target power plant respectively.

[0080] In an optional embodiment, the constraints of the target power plant are as follows:

[0081]

[0082] Among them, st: represents the constraints of the target power plant.

[0083] In an optional embodiment, solving the target optimization model based on an optimization algorithm to obtain a scheduling plan for the target power plant includes:

[0084] Set the initialization parameters of the optimization algorithm and initialize the population position. The population position initialization formula is as follows:

[0085]

[0086] Among them, x t and x t+1 represents the position of the individual at the t-th iteration and the t+1-th iteration, and p represents the preset parameter for controlling the nonlinear mapping;

[0087] Calculate the fitness value of the individual and the position of the elite individual. The calculation formula for the position of the elite individual is as follows:

[0088]

[0089] in, represents the reverse solution of the j-th dimension individual at the t-th iteration, x i,j (t) represents the elite solution of the j-th dimension individual at the t-th iteration, k represents a random number in the interval [0,1], u j (t) represents the maximum position of the individual at the tth iteration, l j(t) represents the minimum position of the individual at the tth iteration;

[0090] Mark a preset number of individuals with the best fitness values ​​as leader individuals, and update the positions of the remaining individuals in the population;

[0091] Calculate and sort the updated individual fitness values, obtain elite individuals, and update the elite individual positions;

[0092] Repeat the above steps until the optimal solution is found or the maximum number of iterations of the optimization algorithm is reached, and the scheduling plan of the target power plant is obtained.

[0093] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A multi-energy coordinated dispatch optimization and emission reduction system for a power plant, comprising a constraint construction unit (2), a first target construction unit (5), a second target construction unit (6), a third target construction unit (7), a fourth target construction unit (8) and a target optimization unit (9), characterized in that: A supply modeling unit (1), the supply modeling unit (1) is used to model the energy supply of the target power plant to obtain an energy supply model of the target power plant, obtain the energy supply amount of the target power plant based on the energy supply model of the target power plant, and transmit the energy supply amount of the target power plant to the constraint construction unit (2); A load prediction unit (3), the load prediction unit (3) being used to perform load prediction on the load end of the target power plant to obtain a load prediction amount of the load end of the target power plant, and transmit the load prediction amount of the load end of the target power plant to a demand modeling unit (4); A demand modeling unit (4), wherein the demand modeling unit (4) receives the load forecast of the load end of the target power plant transmitted by the load forecasting unit (3), and models the energy demand of the load end of the target power plant based on the load forecast of the load end of the target power plant to obtain an energy demand model of the load end of the target power plant, obtains the energy demand of the load end of the target power plant based on the energy demand model of the load end of the target power plant, and transmits the energy demand of the load end of the target power plant to the constraint construction unit (2).

2. According to claim 1, a power plant multi-energy coordinated dispatch optimization emission reduction system is characterized by: The first target construction unit (5) is used to model the operating cost of the target power plant to obtain an operating cost target model of the target power plant, and transmit the operating cost target model of the target power plant to the target optimization unit (9); The second target construction unit (6) is used to model the carbon trading cost of the target power plant to obtain the carbon trading cost target model of the target power plant, and transmit the carbon trading cost target model of the target power plant to the target optimization unit (9).

3. A power plant multi-energy coordinated dispatch optimization emission reduction system according to claim 2, characterized in that: The third target construction unit (7) is used to model the pollutant emission of the target power plant to obtain a pollutant emission target model of the target power plant, and transmit the pollutant emission target model of the target power plant to the target optimization unit (9); The fourth target construction unit (8) is used to model the day-ahead cost of the target power plant to obtain a day-ahead cost target model of the target power plant, and transmit the day-ahead cost target model of the target power plant to the target optimization unit (9).

4. The power plant multi-energy coordinated dispatch optimization emission reduction system according to claim 3 is characterized by: The constraint construction unit (2) receives the energy supply of the target power plant transmitted by the supply modeling unit (1) and the energy demand of the load end of the target power plant transmitted by the demand modeling unit (4), and constructs the constraint conditions of the target power plant based on the energy supply of the target power plant and the energy demand of the load end of the target power plant, and transmits the constraint conditions of the target power plant to the target optimization unit (9); The target optimization unit (9) receives the operating cost target model of the target power plant transmitted by the first target construction unit (5), the carbon trading cost target model of the target power plant transmitted by the second target construction unit (6), the pollutant emission target model of the target power plant transmitted by the third target construction unit (7), the day-ahead cost target model of the target power plant transmitted by the fourth target construction unit (8), and the constraint conditions of the target power plant transmitted by the constraint construction unit (2), and constructs a target optimization model based on the operating cost target model, carbon trading cost target model, pollutant emission target model, day-ahead cost target model and constraint conditions of the target power plant, and solves the target optimization model based on an optimization algorithm to obtain a scheduling plan for the target power plant.

5. The multi-energy coordinated dispatch optimization and emission reduction system for power plants according to claim 4 is characterized by: The energy supply model of the target power plant is as follows: Among them, Q s,t represents the energy supply of the target power plant in period t, I g ,I r and I h Respectively represent the total number of thermal power generating units, gas generating units and hydropower generating units in the target power plant, and They represent the upper and lower limits of the output of thermal power generating units in the target power plant, and They represent the upper and lower limits of the output of the gas-fired generator sets in the target power plant, and They represent the upper and lower limits of the output of the hydroelectric generating units in the target power plant during period t, M g 、M r and M h They respectively represent the climbing capabilities of the thermal power generating units, gas power generating units and hydropower generating units in the target power plant.

6. A power plant multi-energy coordinated dispatch optimization and emission reduction system according to claim 5, characterized in that: The energy demand model of the load side of the target power plant is as follows: Among them, Q d,t represents the energy demand at the load end of the target power plant during period t, q g ,q r and q h They represent the adjustment coefficients caused by the load power prediction errors of the thermal power generating units, gas generating units and hydropower generating units in the target power plant, respectively. and They represent the load power forecast of thermal power generating units, gas generating units and hydropower generating units in the target power plant in the period t+1 respectively, e represents the adjustment coefficient caused by the load forecast error at the load end of the target power plant, and They represent the load forecast of the target power plant at the load end in the t+1 period and the t period respectively.

7. A power plant multi-energy coordinated dispatch optimization and emission reduction system according to claim 6, characterized in that: The target operating cost model of the target power plant is as follows: Among them, min:C ope represents the target power plant operating cost target model, c g 、c r and c h They represent the unit energy purchase costs of thermal power generating units, gas generating units and hydropower generating units in the target power plant respectively, and T represents the preset total time period; The carbon trading cost target model of the target power plant is as follows: Among them, min:C reg represents the carbon trading cost target model of the target power plant, c r,g 、c r,r and c r,g Respectively represent the carbon trading prices of thermal power generation units, gas power generation units and hydropower generation units in the target power plant, ξ g , r and h They respectively represent the carbon emission coefficient per unit of electricity of thermal power generating units, gas power generating units and hydropower generating units in the target power plant.

8. The power plant multi-energy coordinated dispatch optimization emission reduction system according to claim 7 is characterized by: The pollutant emission target model of the target power plant is as follows: Among them, min:F pol represents the pollutant emission target model of the target power plant, a1, a2, a3, a4 and a5 represent the pollutant emission coefficients of the thermal power generating units in the target power plant, and b1, b2, b3, b4 and b5 represent the pollutant emission coefficients of the gas generating units in the target power plant; The day-ahead cost target model of the target power plant is as follows: Among them, min:F day represents the day-ahead cost target model of the target power plant, u g,t 、u r,t and u h,t are Boolean variables representing the operating status of the thermal power generation unit, gas power generation unit and hydropower generation unit in the target power plant during period t. The value is 1 if the operating status is on and 0 if the operating status is off. g,on , C r,on and C h,on They represent the single startup costs of thermal power generating units, gas generating units and hydropower generating units in the target power plant, respectively. g,off , C r,off and C h,off They represent the single shutdown costs of thermal power generating units, gas power generating units and hydropower generating units in the target power plant respectively.

9. A power plant multi-energy coordinated dispatch optimization emission reduction system according to claim 8, characterized in that: The constraints of the target power plant are as follows: Wherein, st: represents the constraint condition of the target power plant.

10. The power plant multi-energy coordinated dispatch optimization and emission reduction system according to claim 1 is characterized by: Solving the target optimization model based on an optimization algorithm to obtain a dispatching plan for the target power plant includes: Set the initialization parameters of the optimization algorithm to initialize the population position. The population position initialization formula is as follows: Among them, x t and x t+1 represents the position of the individual at the t-th iteration and the t+1-th iteration, and p represents the preset parameter for controlling the nonlinear mapping; Calculate the fitness value of the individual and calculate the elite individual position. The elite individual position calculation formula is as follows: in, represents the reverse solution of the j-th dimension individual at the t-th iteration, x i,j (t) represents the elite solution of the j-th dimension individual at the t-th iteration, k represents a random number in the interval [0,1], u j (t) represents the maximum position of the individual at the tth iteration, l j (t) represents the minimum position of the individual at the tth iteration; Marking a preset number of the individuals with the best fitness values ​​as leader individuals, and updating the positions of the remaining individuals in the population; Calculate and sort the updated individual fitness values, obtain elite individuals, and update the elite individual positions; Repeat the above steps until the optimal solution is found or the maximum number of iterations of the optimization algorithm is reached, and the scheduling plan of the target power plant is obtained.

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